Higher Education and Research in Agronomy


R for Statistics

Cornillon P.A., Guyader A., Husson F., Jégou N., Josse J., Kloareg M., Matzner-Løber E., Rouvière L.

(2012) Chapman & Hall/CRC press.

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.

Focusing on the R software, the first section covers:

  • Basic elements of the R software and data processing,
  • Clear, concise visualization of results, using simple and complex graphs,
  • Programming basics: pre-defined and user-created functions.

The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:

  • Regression methods,
  • analyses of variance and covariance,
  • classification methods,
  • exploratory multivariate analysis,
  • clustering methods,
  • hypothesis tests.

After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.


Foreword Table of contents Order

Datasets and lines of code